Mammography is an effective method of screening for breast cancer, a leading cause of mortality among women. However, analyzing mammograms requires skilled radiologists whose performance can be degraded by the demand of viewing a large number of images in a finite amount of time. The computer-aided detection (CAD) feature in many mammography systems has been provided to assist radiologists in capturing true-positives (TP) that might otherwise have been overlooked.
An abnormality in mammograms includes microcalcifications (MCC), which are tiny deposits of calcium in breast carcinoma. It is very difficult to distinguish between malignant and benign microcalcification clusters, even for experienced radiologists, which may lead to a high rate of unnecessary biopsies. Therefore, it is desirable to design the CAD algorithm in such a way that a high TP rate can be achieved while the number of false-positives (FPs) is kept to a minimum. It has been noted that many FP MCC candidates as selected by mammography CAD systems in the past were found to fall on the linear normal structures such as blood vessels in digital or film-based mammograms. Thus, it is believed that removing those MCC candidates that are associated with linear structures will significantly reduce the overall FP rate.
Various methods for extracting linear structures from a mammographic image have been proposed. Zwiggelaar, Parr, and Taylor (R. Zwiggelaar, T. C. Parr, and C. J. Taylor, “Finding orientated line patterns in digital mammographic images,” Proc. 7th Br. Machine Vision Conf., 1996, pp. 715-724.) have compared the performance of several different approaches (including orientated bin and line operator methods) to the detection of linear structures with synthetic mammographic images. Their results suggest significant differences between the different approaches. One approach has been implemented as a multi-scale line operator and gives intuitively convincing results. The output could be used for classifying linear structures.
The work of line operator can be described as follow: The line operator takes the average grey level of the pixels lying on an orientated local line passing through the target pixel and subtracts the average intensity of all the pixels in the locally orientated neighborhood. The line strength is compared for n orientations. Line direction is obtained from the orientation producing the maximum line strength. Scale information can be obtained by applying the line operator to images that are resealed by Gaussian smoothing and sub-sampling. For each pixel, the scale producing the maximum line strength is taken as the detected line scale.
Cerneaz et al. (N. Cerneaz and M. Brady, “Finding Curvilinear Structures in Mammograms,” Lecture Notes in Computer Science, 905, pp. 372-382, 1995) introduced a method that estimates the intensity profile of the curvilinear structures (CLS) in mammograms in a single scale. In this method, the CLS are assumed to have circular cross section when the breast is not compressed. And the cross section of CLS in mammogram is assumed to be elliptical. Candidate pixels for CLS are detected using the response of a second order difference operation which is applied in four directions. If there is a sufficient high response for one of the orientations the pixel will form part of a CLS. A measure of line strength is obtained by determining the contrast of the line profile at these pixels.
Wai et al. (A Multi-resolution CLS Detection Algorithm for Mammographic Image Analysis,” Medical Imaging Computing and Computer-Assisted Intervention, MICCAU, pp. 865-872, 2004) adopted the two-step method from Cerneaz's work and devised a multi-resolution ridge detector for structures ranging from 1800 microns to 180 microns. Wai et al. also enhanced the performance of the detector by using a local energy thresholding to suppress undesirable response from noise. The local energy is also used to determine CLS junctions.
Alexander Schneider et al., in U.S. Patent Application Publication No. US20020159622, proposed a system and method for detecting lines in medical images. In their method, a direction image array and a line image array are formed by filtering a digital image with a single-peaked filter, convolving the regular array with second-order difference operators oriented along the horizontal, vertical, and diagonal axes, and computing the direction image arrays and line image arrays as direct scalar functions of the results of the second order difference operations. As best understood, line detection based on the use of four line operator functions requires fewer computations than line detection based on the use of three line operator functions, if the four line operator functions correspond to the special orientations of 0, 45, 90 and 135 degrees.
For the issue of FP reduction, a paper by Zhang et al. (“A New False Positive Reduction Method for MCCs Detection in Digital Mammography,” Acoustics, Speech and Signal Processing 2001, Proc. IEEE Intl. Conf. on (ICASSP), V. 2, Issue 2001, pp. 1033-1036, 2001) describes a mixed feature multistage FP reduction algorithm utilizing eleven features extracted from spatial and morphology domains. These features include gray-level description, shape description and clusters description but no feature is directly related to linear structures. Wai et al. mention in their article that the results from the multi-resolution ridge detector could be beneficial to microcalcification false-positive reduction but the realization of the reduction is absent. Moreover, it is computationally inefficient to generate actual linear structures just for the purpose of confirming the association of an MCC candidate cluster with a linear structure in mammography CAD.
Therefore, a need exists for an improved approach for image linear structure verification in mammography.
The present invention is designed to overcome the problems set forth above. More particularly, with the present invention, all MCC candidate clusters are assumed being associated with linear structures until verified otherwise. Therefore, the present invention provides a method for linear structure (LS) verification in mammography CAD systems with the objective of reducing microcalcification (MCC) false-positives. The method of the invention is MCC cluster driven method and verifies linear structures with a small rotatable band centered around a given MCC candidate cluster in question. The classification status of an MCC candidate cluster is changed if its association with a linear structure is confirmed through the LS verification. There are mainly four identifiable features that are extracted from the rotatable band in the gradient magnitude and Hough parameter spaces. The LS verification process applies cascade rules to the extracted features to determine if an MCC candidate cluster resides in a linear structure area.